The RLLChatbot: a solution to the ConvAI challenge

Abstract

Current conversational systems can follow simple commands and answer basic questions, but they have difficulty maintaining coherent and open-ended conversations about specific topics. Competitions like the Conversational Intelligence (ConvAI) challenge are being organized to push the research development towards that goal. This article presents in detail the RLLChatbot that participated in the 2017 ConvAI challenge. The goal of this research is to better understand how current deep learning and reinforcement learning tools can be used to build a robust yet flexible open domain conversational agent. We provide a thorough description of how a dialog system can be built and trained from mostly public-domain datasets using an ensemble model. The first contribution of this work is a detailed description and analysis of different text generation models in addition to novel message ranking and selection methods. Moreover, a new open-source conversational dataset is presented. Training on this data significantly improves the Recall@k score of the ranking and selection mechanisms compared to our baseline model responsible for selecting the message returned at each interaction.

arXiv comment46 pages including references and appendix, 14 figures, 12 tables; Under review for the Dialogue & Discourse journal

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• ADADELTA: An Adaptive Learning Rate Method
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• A Neural Conversational Model
• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Text Understanding from Scratch
• Deep Reinforcement Learning for Dialogue Generation
• A Deep Reinforcement Learning Chatbot
• A Survey of Available Corpora for Building Data-Driven Dialogue Systems
• Learning to Ask: Neural Question Generation for Reading Comprehension
• A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
• Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner